ServiceNow's AI-Powered Q3 Surge Proves Enterprise AI ROI: Now Assist Drives $3.26B Revenue Forecast
ServiceNow's Q3 revenue surge illustrates the tangible ROI of AI, showcasing how effective implementation can transform enterprise operations.
By 2025, nearly half of business leaders view proving generative AI's business value as their top challenge, according to Gartner. Meanwhile, S&P Global reports a sharp increase in abandoned AI projects, with 42% of companies citing unclear ROI as the primary reason. Yet, ServiceNow’s Q3 2025 revenue forecast of $3.26 billion highlights a different story - AI can deliver measurable results when implemented effectively. Their Now Assist platform not only exceeded expectations for net new Annual Contract Value (ACV) but also demonstrated how AI can streamline operations and drive revenue growth.
In this article, you'll discover:
- How ServiceNow leveraged AI-powered workflow automation to boost deal volumes and average contract values.
- The key challenges enterprises face in proving AI ROI and actionable strategies to overcome them.
- Practical steps to implement AI-driven sales automation tools that deliver measurable financial outcomes.
With enterprise AI adoption accelerating and the global sales automation market forecasted to reach $16 billion by year-end, understanding how to achieve ROI is more critical than ever. ServiceNow’s success offers a roadmap for businesses aiming to capitalize on AI’s potential. Let’s explore how your organization can replicate this approach to drive growth and efficiency.
ServiceNow stock jumps: CFO says AI is 'real in our business'
The Problem: Enterprise AI ROI Measurement Challenges
While ServiceNow's success with Now Assist showcases the potential of AI to deliver measurable returns, many enterprises face significant hurdles in assessing the true impact of their AI initiatives. Unlike traditional technologies with straightforward metrics for cost savings or revenue growth, AI’s value often spans multiple teams and processes, making it harder to quantify.
A key challenge lies in aligning traditional business metrics with the complex outcomes AI delivers. Research highlights that 73% of organizations struggle to define the specific impact or metrics of their digital initiatives, creating a bottleneck that slows the scaling of enterprise automation. Identifying the right metrics and determining when to measure them are critical steps to overcoming this hurdle. As Jacob Axelsen, an AI expert at Devoteam Denmark, points out:
"Measuring AI ROI requires a deeper understanding of the business process and its specific metrics".
Another significant barrier is data quality. Poor data integrity makes it difficult to establish clear connections between AI deployments and business results. In fact, 85% of organizations anticipate that data quality will be their biggest challenge in executing AI strategies by 2025. Inconsistent or incomplete data undermines the ability to track AI's contributions effectively.
Organizational readiness also plays a major role in ROI challenges. Only 8% of organizations rate their AI initiatives as "extremely successful", and as many as 80% of AI projects fail to meet their intended goals or deliver measurable returns. Fragmented implementations and lack of cohesive strategies often prevent enterprises from realizing the full potential of their AI investments.
Operational inefficiencies further complicate the picture. Without addressing bottlenecks in existing workflows, AI solutions can fail to deliver meaningful results. Issues such as data integrity problems and process delays reduce the overall impact, creating additional barriers to achieving significant ROI.
Scaling AI across an organization presents yet another challenge. Only 2% of global enterprises are well-prepared to scale AI securely, leaving most companies stuck in isolated pilot projects. Gartner predicts that 30% of generative AI projects will stall after the proof-of-concept phase due to challenges related to data quality, costs, risks, or unclear value. These limitations highlight the importance of a cohesive, enterprise-wide AI strategy - something ServiceNow has exemplified.
The path to unlocking measurable AI returns requires addressing these challenges head-on. From improving data quality to aligning metrics with business goals and overcoming scaling barriers, enterprises must adopt integrated strategies to ensure AI investments deliver meaningful, quantifiable results.
The Solution: How Now Assist Drives Financial Results
Now Assist addresses traditional ROI challenges by seamlessly integrating into existing workflows, delivering clear, measurable outcomes. Its role in ServiceNow's projected $3.26 billion Q3 revenue highlights its impact.
By combining generative AI with workflow automation, Now Assist effectively resolves key obstacles to scaling AI. This integration lays the groundwork for sustainable, cross-functional growth, supported by its advanced workflow automation capabilities.
AI-Powered Workflow Automation
Now Assist revolutionizes complex business processes through intelligent automation that extends beyond basic task execution. Its generative AI capabilities are integrated with workflow automation to offer features such as summarization, conversational tools, content creation, code generation, custom skill development, and AI-driven search. These tools work together to eliminate manual bottlenecks, enabling organizations to realize the full potential of AI.
What sets Now Assist apart is its domain-specific approach. ServiceNow has developed its proprietary Now LLMs while also supporting third-party models, ensuring AI agents can autonomously learn, collaborate, and act to resolve issues and boost productivity. This tailored design allows the platform to grasp the intricacies of enterprise workflows, which general-purpose AI tools often fail to capture.
Real-world use cases further validate its automation capabilities. In May 2025, ServiceNow introduced specialized AI agents for various functions, including IT service management (ITSM), IT operations management (ITOM), IT asset management (ITAM), strategic portfolio management (SPM), operational technology (OT), and data foundation. For instance, ITSM agents streamline incident management with real-time communication, while ITOM agents handle tasks like alert triage and root cause analysis autonomously. ITAM agents manage software and hardware procurement while ensuring compliance, and SPM agents monitor project progress, notifying managers of delays. These innovations significantly enhance operational efficiency across organizations.
Operational Efficiency and Sales Changes
Improvements in operational workflows directly enhance sales productivity and customer satisfaction. By implementing sales automation through Now Assist, companies can reduce up to 60% of the time spent on non-revenue-generating tasks. This allows sales teams to focus on activities that drive revenue growth.
The BT Group offers a compelling example of swift deployment. Alex Bell, Business Service CIO at BT Group, shared:
"While everyone has been talking about GenAI, we've been putting it to work for our people and our customers. We've been able to move fast because it's built right into the ServiceNow platform".
Enhanced customer service also creates new revenue opportunities. Kainos, for instance, has used Now Assist's knowledge management tools to improve customer self-service. Peiter la Cour Freiesleben, Director of Application Management and Strategic Growth, EMEA at Kainos, stated:
"We have a lot of knowledge content that is powered by Now Assist's GenAI, so customers can find what they need faster".
Mark Blyth, Head of Business Solutions at Mears Group, emphasized the broad benefits:
"Now Assist is a game changer, in the user experience and in saving time and money for the business".
Financial Impact Data
The operational improvements achieved by Now Assist translate directly into measurable financial gains, reinforcing its ROI value. ServiceNow's Q2 2025 financial results highlight its revenue-driving capabilities. Now Assist consistently exceeded net new Annual Contract Value (ACV) expectations, with adoption increasing across multiple product lines. For example, ITAM Now Assist’s net new ACV grew nearly sixfold quarter over quarter, while SecOps and Risk solutions more than doubled their net new ACV in the same period.
Across all product categories, deal volumes surged - doubling, tripling, or even quadrupling in some cases - demonstrating growing customer investment in AI-driven automation. Creator Now Assist, for instance, saw its average deal size quadruple year over year.
These advancements are reflected in ServiceNow's overall financial performance. Q2 subscription revenues reached $3.113 billion, a 21.5% increase year over year on a constant currency basis. The company raised its FY2025 subscription revenue midpoint by $125 million and projects Q3 revenues to range between $3.260 billion and $3.265 billion.
Gina Mastantuono, ServiceNow’s President and CFO, underscored the platform’s growth trajectory:
"Now Assist continued to surpass net new ACV expectations, fueled by an increase in both deal volume and size quarter‑over‑quarter, putting us firmly on track to hit our $1 billion ACV target by 2026".
Additionally, the sales pipeline generated from the Knowledge 2025 event exceeded $1.2 billion, signaling strong demand for AI-driven automation solutions.
These results demonstrate that with proper implementation and scaling, enterprise AI can deliver measurable financial returns across business functions.
Business Lessons: How to Copy ServiceNow's AI Success
ServiceNow's journey offers a clear example of how to achieve measurable returns with AI. Their approach underscores the importance of thorough preparation over quick fixes, setting a strong foundation for successful AI implementation.
The company’s unified platform integrates data, workflows, and governance into a single framework. CEO Bill McDermott describes this approach as:
"AI plus data plus workflows on one fully integrated platform that replaces all of the chaos with clarity".
This seamless integration eliminates data silos, a common stumbling block for AI projects, and ensures consistent results across departments.
ServiceNow’s phased strategy includes Predictive Intelligence, Now Assist, and Agentic AI. Supporting these initiatives, the AI Control Tower acts as the central governance hub, ensuring that AI efforts are managed cohesively.
Preparing for AI Implementation
Before diving into AI, organizations must focus on data quality and governance. ServiceNow demonstrates that a solid foundation is essential for AI to thrive.
The first step is creating a unified data architecture. ServiceNow’s platform, powered by integrated IT Service Management, ensures this consistency. Amit Zavery, the company’s President, Chief Product Officer, and COO, highlights the importance of this architecture:
"Our platform architecture is purpose built to scale across the enterprise. It performs consistently, whether you are automating a single process or an entire business. With the CMDB at its core, it offers a consolidated real time view of every asset across your company."
Organizations should also form a cross-functional AI governance committee. This group, which includes representatives from IT, operations, legal, and business units, ensures that AI initiatives align with organizational goals and mitigate risks.
"Companies that outperform on growth invest more aggressively in digital-led transformations and AI to boost sales and marketing productivity".
Investing in data validation, standardization, and cleaning is another critical step. ServiceNow’s acquisition of Data.world to enhance data cataloging and governance capabilities illustrates the importance of robust data management.
Equally important is change management. Including employees in discussions about AI implementation from the start fosters trust and surfaces potential challenges:
"Including employees in discussions about AI implementation from the beginning builds trust and helps identify real challenges".
Clear communication about the purpose of AI initiatives and their benefits for both the company and employees, combined with training programs, helps staff adapt and acquire the skills needed to work alongside AI systems. These efforts are essential to replicating the financial success of tools like Now Assist.
Best Practices for Global Sales Automation
Once a strong data foundation is in place, organizations can focus on scaling AI-driven strategies for global sales. ServiceNow’s success with Now Assist highlights how AI can enhance sales performance:
"AI can help identify high-potential opportunities and avoid low-return efforts".
Starting with pilot programs allows businesses to test AI solutions on a smaller scale before making significant investments. For example, a logistics company uploaded over one billion data records into an AI-powered product recommendation engine. This system segmented customers by location and buying habits, identifying the top three cross-selling opportunities across categories.
"Based on early results, the logistics company anticipates increasing annual sales by $100 million".
Integrating AI tools into existing CRM systems is another key strategy. AI-driven sales agents, for instance, have been shown to boost revenue by 15% on average, while:
"AI-powered sales tools can save sales teams up to 40% of their time".
Real-time data integration is especially valuable for global operations. A heavy-equipment distributor implemented a generative AI solution that processed over 13,000 company documents. This tool, embedded in their CRM platform, provided instant answers via a customer service chatbot, offering sales representatives actionable insights based on company knowledge and customer context.
"The implementation resulted in a 90% decrease in average resolution time (from 15 minutes to less than one minute) and a ten-percentage-point improvement in finding the right resolution on the first attempt".
AI also transforms pricing strategies. A metal packaging company used an advanced pricing management tool to analyze microsegments like contract types, orders, and customer industries. By identifying margin leakages in real-time, account executives could incorporate insights directly into negotiations.
"The result was a 3% improvement in margins over two years due to more effective pricing".
To maximize the impact of AI, organizations should define SMART (Specific, Measurable, Achievable, Relevant, Time-bound) goals. Key metrics such as conversion rates, sales cycle length, customer acquisition costs, and customer satisfaction should guide AI implementation.
"Companies leveraging AI in sales automation are seeing a 10-20% increase in ROI".
This shows that with the right strategies and focus, AI can deliver measurable improvements in sales and overall business performance.
How to Choose AI-Powered Sales Automation Tools
Picking the right AI-powered sales automation tool requires more than just a glance at its features. With 81% of sales teams already exploring or fully implementing AI solutions, the market is teeming with options, making a thoughtful selection process essential for delivering measurable results. Companies that align their tools with their specific needs and existing systems often see a 10–20% increase in ROI. As the global sales automation market is projected to hit $16 billion by 2025, the stakes - and opportunities - are higher than ever.
Key Features to Prioritize
To make a meaningful impact, AI sales tools must offer capabilities that simplify and enhance your sales processes. Here are the critical features to consider:
- Seamless Integration: Any chosen solution should easily connect with your existing CRM, marketing tools, and other systems. This ensures that workflows remain intact and uninterrupted.
- Personalization: Tools that analyze customer behavior, past purchases, and engagement data can deliver tailored recommendations and automate workflows for better outcomes.
- Advanced Analytics: A robust reporting dashboard is crucial. It should track KPIs like conversion rates, sales cycle duration, and customer acquisition costs, offering real-time insights for smarter decision-making.
- Scalability: A tool that grows with your business eliminates the need for costly system overhauls. Whether it’s handling more users, data, or sales territories, scalability is a must.
- Context Awareness: Advanced platforms should adapt to your brand voice and organizational data, producing outputs that align with your company’s goals and industry standards.
By focusing on these features, you can ensure the tool you choose aligns with your business needs and supports long-term growth.
Steps to Evaluate and Implement Solutions
After identifying the must-have features, it’s time to evaluate potential tools. Start by reviewing your current workflows and integration requirements. During the proof-of-concept (POC) phase, assess how well the tool integrates with your systems to avoid surprises later.
Testing the tool in real business scenarios is invaluable. Engage internal users during the POC to gather feedback on usability and workflow compatibility. This hands-on approach can reveal potential challenges that vendor demonstrations might not highlight.
A well-chosen tool should enhance, not disrupt, your existing workflows. Solutions with robust APIs and developer support minimize implementation hurdles. Equally important is the user experience - if non-technical teams find the tool difficult to use, its potential impact may be limited.
To maximize the tool’s effectiveness, establish clear governance processes for AI adoption. Define guidelines for requesting new tools and create systems for collecting and acting on user feedback during and after pilot testing. This structured approach ensures smoother adoption and ongoing optimization.
Conclusion: The Future of Enterprise AI and Sales Automation
ServiceNow's projected $3.26 billion revenue, driven by its Now Assist platform, highlights a major shift in enterprise AI adoption. This achievement serves as a roadmap for businesses worldwide, demonstrating that AI-powered sales automation is no longer a distant concept - it’s a current competitive edge. Companies across industries are already following this example to stay ahead.
By 2027, 95% of seller research workflows are expected to start with AI, a sharp rise from less than 20% in 2024. This shift underscores the growing impact of AI, with businesses that integrate these tools reporting up to 20% gains in sales productivity and a 15% boost in revenue. Additionally, with 80% of B2B sales interactions predicted to occur through digital channels by 2025, delaying AI adoption could leave companies struggling to compete in an increasingly automated landscape.
Data-driven sales strategies are proving their worth, delivering a 25% increase in revenue, while trigger-based automation achieves conversion rates 25% higher than traditional time-based approaches. These improvements are powered by AI’s ability to deliver hyper-personalized experiences, smarter lead qualification, and integrated omnichannel engagement - capabilities that are quickly becoming standard expectations rather than differentiators.
Harvard Business School Professor Karim Lakhani underscores the urgency of this transformation:
"I have a strong belief that the future of business is going to be AI-powered".
This sentiment is backed by the fact that 92% of companies plan to expand their AI investments, inspired by the proven ROI from tools like ServiceNow’s Now Assist. These trends highlight the importance of turning AI enthusiasm into measurable business outcomes.
To succeed, companies must focus on solutions that deliver tangible results. Organizations implementing integrated AI strategies report ROI improvements of 22% for customer service center (CSC) development and 30% for generative AI integration. The key lies in selecting tools that integrate smoothly with existing systems, provide actionable analytics, and scale alongside business growth - an approach exemplified by ServiceNow’s success.
Looking ahead, the real question isn’t whether AI will reshape sales automation but how quickly businesses can adopt these tools to secure their share of the projected $16 billion sales automation market. ServiceNow’s Now Assist story offers a proven path forward, with results that speak volumes about the potential of AI-driven transformation.
FAQs
How does ServiceNow's Now Assist help enterprises measure the ROI of their AI initiatives?
ServiceNow's Now Assist makes it easier to evaluate the return on investment (ROI) for AI by providing tools specifically designed to track and showcase the value of AI initiatives. A standout feature is its dedicated value dashboard, which delivers clear, actionable metrics to assess the performance of AI-driven capabilities like Now Assist skills, enabling businesses to measure their impact effectively.
The platform also offers robust governance tools, including the AI Control Tower, which centralizes oversight and management of AI projects. This feature helps organizations monitor financial outcomes, track ROI, and make informed, data-backed decisions to enhance the effectiveness of their AI strategies.
What steps can businesses take to successfully implement and scale AI across their organization?
To make the most of AI in your business, it's crucial to start with clear goals and measurable KPIs that align with your overall strategy. This ensures AI initiatives are purpose-driven and tied to tangible outcomes. It's wise to begin with smaller pilot projects to evaluate AI solutions in action before rolling them out across the organization.
Another important step is investing in scalable data management systems to effectively handle the increasing volume of data. Building a team with the right AI expertise, gaining strong leadership backing, and regularly refining AI use cases based on measurable ROI are also critical for long-term success. Adopting a phased and well-thought-out approach will help drive sustainable growth and improve operational efficiency.
How can businesses integrate AI tools like Now Assist into their workflows to boost ROI effectively?
To make the most of AI tools like Now Assist and boost your ROI, begin by evaluating your existing workflows. Look for bottlenecks or repetitive tasks that could benefit from automation. Key areas to consider include lead scoring, follow-ups, and automating routine processes - these are often where AI can deliver the greatest value.
Once you've identified these opportunities, focus on preparing your team to leverage the AI effectively. Providing proper training and clear communication is crucial. When employees understand how AI fits into their roles and enhances their efficiency, they’re more likely to embrace it. Roll out the tool in stages, giving your team time to adapt and refine processes. This phased approach helps minimize disruptions, ensures smoother integration, and leads to noticeable improvements in both productivity and revenue.